The present application relates, generally, to an improved data processing apparatus and method and, more specifically, to mechanisms for optimization of mixed-criticality systems.
Problems of optimization under uncertainty are characterized by the necessity of making decisions without knowing what the decision's full effects will be. Such problems appear in many areas of application and do present many interesting challenges. Traditional models of decision-making under uncertainty assume distributional information about random variables, or empirical approximations thereof, are available. Often, one assumes empirically, but infinitely accurately observed values of a random variable are available, among which there is no ordering. Nevertheless, an observation of an extreme value may yield a different response than observations concentrated around the empirical mean, and precise means of measurement are rarely available in practice. Any approach based on poor modeling and erroneous inputs might be infeasible or exhibit poor performance when implemented.